class_docstr: A pretrained model implementation for Grapheme-to-Phoneme (G2P) models
  that take raw natural language text as an input and
  
  Example
  -------
  >>> text = ("English is tough. It can be understood "
  ...         "through thorough thought though")
  >>> from speechbrain.inference.text import GraphemeToPhoneme
  >>> tmpdir = getfixture('tmpdir')
  >>> g2p = GraphemeToPhoneme.from_hparams('path/to/model', savedir=tmpdir) # doctest: +SKIP
  >>> phonemes = g2p.g2p(text) # doctest: +SKIP
docstr: Loads any relevant model dependencies
docstr_tok: ["Loads","any","relevant","model","dependencies"]
code: def load_dependencies(self):
      """Loads any relevant model dependencies"""
      deps_pretrainer = getattr(self.hparams, "deps_pretrainer", None)
      if deps_pretrainer:
          deps_pretrainer.collect_files()
          deps_pretrainer.load_collected(device=self.device)
  
imports: import torch
  from itertools import chain
  from speechbrain.inference.interfaces import Pretrained, EncodeDecodePipelineMixin
label_desc: Use image node_id 6 for calling the GraphemeToPhoneme obj's underlying member method code with example usage: obj.load_dependencies() without return types